1. Field of the Invention
The present invention generally relates to automated inspection of digitized microscope slides and more particularly relates to pattern recognition techniques employed in automated inspection of virtual slides.
2. Related Art
An obstacle to automating microscopic inspection has been the inability to efficiently digitize entire microscope specimens at diagnostic resolutions. Conventional approaches for creating virtual slides have relied on image tiling. Image tiling involves the capture of multiple, small regions of a microscope slide using a traditional charge coupled device (“CCD”) camera. These tiles are typically “stitched” together (aligned) to create a large contiguous digital image (mosaic) of an entire slide. A minimum of 2,250 individual camera tiles are required to digitize a typical 15 mm×15 mm area of a slide at 50,000 pixels/inch (0.5 μm/pixel).
Image tiling carries several disadvantages. First, images frequently have distortion because image tiles are limited to a single focal plane from the camera's fixed area objective lens. Second, an image tiling system produces optical aberrations that are circularly symmetric about the center of the image tile. Third, full pixel resolution is usually unavailable because color CCD cameras lose spatial resolution upon interpolation of color values from non-adjacent pixels.
Conventional approaches to pattern recognition are also cumbersome. Despite many years of improvements in optical microscopy, in most cases a human operator is still required to manually evaluate a specimen through the eyepieces of a dedicated instrument. Worldwide, in thousands of clinical laboratories, more time is spent performing manual microscopy than any other in-vitro diagnostics testing procedure. In an estimated 20,000 research laboratories, manual microscopic inspection is an essential tool for screening drug targets and for conducting toxicology studies. While offering immense opportunities for automation, microscopic inspection remains a bastion of manual labor in an environment that is otherwise converting to automated solutions.
Some attempts at computer aided pattern recognition have been made. These approaches to pattern recognition in microscopic images are based on morphological features. A large number of feature metrics (e.g., cell size, nuclear-to-cytoplasmic ratio, roundness, density, color, texture, etc.) are computed to identify “objects” (e.g., cells) that appear in the image. Pattern recognition is achieved by correlating the feature metrics for an unknown object with those of a known object. Feature based approaches have had some success in cytology, where a high level of cell diversity is required to allow objects to be identified. These conventional approaches, however, are immensely complicated or completely untenable for histology imagery data where reliable object segmentation is difficult.